Abstract The Surrogate Modeling Toolbox (SMT) is an open-source Python package that
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …

Reservoir computing is a machine learning method that is closely linked to dynamical
systems theory. This connection is highlighted in a brief introduction to the general concept …

Forecasting the behavior of high-dimensional dynamical systems using machine learning
requires efficient methods to learn the underlying physical model. We demonstrate …

Time-delay reservoir computing (TDRC) represents a simplified variant of recurrent neural
networks, employing a nonlinear node with a feedback mechanism to construct virtual …

Drawing on ergodic theory, we introduce a novel training method for machine learning
based forecasting methods for chaotic dynamical systems. The training enforces dynamical …

The simulation of turbulent flow requires many degrees of freedom to resolve all the relevant
times and length scales. However, due to the dissipative nature of the Navier-Stokes …

Forecasting chaotic systems is a notably complex task, which in recent years has been
approached with reasonable success using reservoir computing (RC), a recurrent network …

In this paper, we investigate the fractal-fractional Malkus Waterwheel model in detail. We
discuss the existence and uniqueness of a solution of the fractal-fractional model using the …

Malaysia has experienced public health issues and economic losses due to air pollution
problems. As the air pollution problem keeps increasing over time, studies on air quality …

The immense computational cost of traditional numerical weather and climate models has
sparked the development of machine learning (ML) based emulators. Because ML methods …